The human brain is mostly made up of neurons, or brain cells, which are connected to by internal wiring called ‘synapses’. Neural networks model how information is passed in between the neurons to “acquire knowledge and learn new things” on their own, says Chun.

Why is this important?

Neural networks are better than other AI techniques at solving problems that are “perceptual in nature”, like speaking and seeing. “You see a lot of neural network research focusing on speech recognition, speech generation, vision, recognising faces, recognising images,” Chun says. Essentially, neural networks excel at recognising patterns, he adds.

“You see a lot of neural network research focusing on speech recognition, speech generation, vision.”

It may take “millions of lines” of code to program more intelligent AI systems. But as neural networks learn on their own, engineers need to merely write a fraction of that, “maybe hundreds of lines”, according to Chun.

Importantly, this has given rise to another field called “deep learning”, which are a more advanced version of neural networks. Deep learning requires a huge amount of computing power, with faster processors and more complex neural structures, says Chun. “To be able to process a massive amount of data, that’s very useful,” he points out.

And tremendous processing power allows neural networks to achieve remarkable feats. In real life, deep learning is behind Tesla’s self-driving cars, which teach themselves by watching footage of human drivers from all over the world, Chun says.

And AlphaGo, the AI program that recently beat the world champion of the Go board game, learned its moves “from watching all games ever played by humans online”, he adds.

How does it affect government?

In the future, Chun predicts that governments “will be able to learn different things much more accurately about its citizens in living habits; in work habits; in health needs; in transportation needs”. “Prediction will be a key benefit from this deep learning,” he adds.

He identifies health benefits in particular: “By analysing and learning health patterns of citizens, it would be able to maybe offer programmes to prevent citizens from getting sick in the first place.”

At present, “the biggest hurdles” are policies and regulations, Chun notes. “Once you have AI smart enough to make decisions, then governments need to figure out how to assign responsibility,” he says, in the event that a faulty self-driving car gets into a crash, for example.

But it is important to note that as governments use citizen data to learn and extract knowledge, citizens must then ask themselves “what are the privacy laws, to what degree our personal data could be used in this manner”, Chun adds.

What is happening in the region?

In Hong Kong, the government has been providing funds to encourage startups to focus on deep learning, Chun says.

Singapore’s Government Technology Agency has identified deep learning as a key focus for 2017. “We are developing our machine and deep learning capabilities to do more in the area of video analytics, unstructured data and cognitive assistance with chatbots and other AI-related tools,” Jacqueline Poh, CEO of the agency, toldGovInsider.

Meanwhile, in China, engineers at search engine Baidu had built a chatbot to assist doctors in answering patients’ questions and suggesting treatment options. The bot was built using deep learning and natural language processing.

And a team from Northeastern University in China have recently developed a neural network that can identify the location of faulty signals in microgrids, which are smaller grids that are connected to the main power grid but can function without it.

Lastly, in Australia, researchers at Murdoch University have developed a neural network to detect dugongs in aerial images of the sea, in a bid to work out the population, size and location of these creatures.

While neural networks are not quite on the Avengers’ level yet, they could in future ‘save’ the world in a different way—solving problems that affect all our lives.